The purpose of this file is processing the combined data files for Winter 2022 into files that contain only valid data for analysis, excluding invalid sessions and participants

Data is imported from 2 files, indicating two levels of analysis: participants and blocks (item-level).

Note: mouse-cursor data contained in final_mouse_blocks.json file is not handled here.

#IMPORT DATA
df_participants <- fromJSON("input/winter22_sgc4a_final_participants.json")
df_items <- fromJSON('input/winter22_sgc4a_final_items.json')

#add term indicator
df_participants$term <- "winter22"
df_items$term <- "winter22"

#DEFINE SGC_4A validity crieria
sessions <- c('wi22sona') #SGC4A second online replication on SONA 
conditions <-c(11111,113,114,115) #4 conditions
violation_threshold = 3 #number of allowable browser violations
effort_exclusion = c("I didn't try very hard, or rushed through the questions", "I started out trying hard, but gave up at some point")
n_items = 15 #fifteen items is complete dataset per participant

#placeholder for excluding participants
ex_participants = data.frame()
#create factors in PARTICIPANTS
df_participants <- df_participants %>%  
  mutate( #create factors and remove extraneous ""
    subject=factor(subject),
    condition=factor(condition),
    study = factor(study),
    condition = factor(condition),
    session = factor(session),
    exp_id = factor(exp_id),
    sona_id = factor(sona_id),
    pool = factor(pool),
    mode = factor(mode),
    attn_check = factor(attn_check),
    status=factor(status),
    term=factor(term),
    gender = as.factor(gender),
    age = as.integer(age),
    country = gsub('"',"",country),
    year = factor(schoolyear),
    major = factor(major),
    browser = factor(browser),
    os = factor(os),
    native_language = factor(language),
    totaltime_m = totaltime/1000/60,
   ) %>% select( #order cols 
    subject,
    study,
    condition,
    session,
    exp_id,
    sona_id,
    pool,
    mode,
    attn_check,
    explanation,
    effort,
    difficulty,
    confidence,
    enjoyment,
    other,
    age,
    country,
    language,
    schoolyear,
    major,
    gender,
    disability,
    browser,
    width,
    height,
    os,
    starttime,
    status,
    term,
    violations,
    absolute_score,
    discriminant_score,
    tri_score,
    orth_score,
    other_score,
    blank_score,
    totaltime_m
   )  
df_items <- df_items %>% 
  mutate(
    subject=factor(subject),
    condition=factor(condition),
    pool=factor(pool),
    mode = factor(mode),
    explicit=factor(explicit),
    impasse = factor(impasse),
    grid = factor(grid),
    mark = factor(mark),
    ixn = factor(ixn),
    term=factor(term),
    relation = factor(relation),
    block = factor(block),
    correct = factor(correct),
    q=factor(q),
    rt_s = rt/1000,
    time_elapsed_m = time_elapsed/1000/60
  ) %>% select(
     subject,
     study,
     term,
     pool,
     mode,
     block,
     explicit,
     impasse,
     grid,
     mark,
     ixn,
     gwidth,
     gheight,
     graph,
     time_elapsed_m,
     question,
     relation,
     q,
     correct,
     discriminant,
     tri_score,
     orth_score,
     other_score,
     blank_score,
     answer,
     rt_s,
     condition
   )

1 Data Validation

1.1 Exclusions

1.1.1 Completion Status

Starting with Winter 2022, data are saved to the database even if the subject’s browser did not meet minimum specifications (at which point they are prompted to change browsers, or end the study). This allows us to learn about the browsers, screen sizes and OS that (potential) subjects are using. However, these data are not exported from the database for analysis (see flatten.js and status.js scripts). Thus, only subjects who successfully completed the entire study are included in this file.

#MANUALLY INSPECT status
df_participants %>% group_by(status) %>% 
  dplyr::summarize(n=n())
## # A tibble: 1 × 2
##   status      n
##   <fct>   <int>
## 1 success   582

582 successfully completed the study.

#DISCARD participants from invalid sessions 
exclude_status <- df_participants %>% 
          filter(status != "success") %>% 
          mutate(reason="invalid-status")

ex_participants <- rbind(ex_participants, exclude_status)
rm(exclude_status)     

df_participants <- df_participants %>% 
  filter( ! subject %in% ex_participants$subject)

No data need to be excluded on account of completion status.

1.1.2 Conditions

Participants are randomly assigned to an experimental condition when starting the study. Here we validate that only conditions for the current study are included in this dataset.

#MANUALLY INSPECT conditions
df_participants %>% group_by(condition) %>% 
  dplyr::summarize(n=n())
## # A tibble: 4 × 2
##   condition     n
##   <fct>     <int>
## 1 11111       238
## 2 113         119
## 3 114         105
## 4 115         120

Data from conditions not corresponding to valid conditions should be discarded.

Split Control Condition

238 subjects were collected under SGC4A condition 11111. However, half of these subjects were actually SGC4B (also control condition, identical design), but the study code was hardcoded. Thus, we need to split and take half the SGC4A 11111 participants, remove them from this dataset, and export them to be moved to SGC4B.

#SPLIT PARTICIPANT FILE

#divide df_participants into control and experimental conditions
df_participants_control <- df_participants %>% filter(condition == "11111")
df_participants_experimental <- df_participants %>% filter(condition %in%  c("113","114","115"))

#split control condition participants into two halves
df_participants_keep <- df_participants_control[seq_len(nrow(df_participants_control)) %% 2 == 0, ]   # Extracting even rows
df_participants_split <- df_participants_control[seq_len(nrow(df_participants_control)) %% 2 == 1, ]   # Extracting odd rows

#validate keep + split = control 
nrow(df_participants_control) == nrow(df_participants_keep) + nrow(df_participants_split)
## [1] TRUE
#RECONSTRUCT DF_PARTICIPANTS
df_participants <- rbind(df_participants_experimental, df_participants_keep)

#SPLIT ITEMS FILE
df_items_keep <- df_items %>% filter(subject %in% df_participants$subject)
df_items_split <- df_items %>% filter(subject %in% df_participants_split$subject)

#validate all items have been split
nrow(df_items) == nrow(df_items_keep) + nrow(df_items_split)
## [1] TRUE
#WRITE SGC4B FILES

#set study = SGC4B
df_participants_split$study <- "SGC4B"
df_items_split$study <- "SGC4B"

#export other half control for SGC4B
write.csv(df_participants_split,"output/winter22_sgc4b_CONTROL_participants.csv", row.names = FALSE)
write.csv(df_items_split,"output/winter22_sgc4b_CONTROL_items.csv", row.names = FALSE)

#RECONSTRUCT DF_ITEMS
df_items <- df_items_keep

#cleanup temp dataframes
rm(df_participants_control,df_participants_experimental, df_participants_keep, df_participants_split, df_items_keep, df_items_split)

After splitting the extra control condition participants into SGC4, we are left with:

#MANUALLY INSPECT conditions
df_participants %>% group_by(condition) %>% 
  dplyr::summarize(n=n())
## # A tibble: 4 × 2
##   condition     n
##   <fct>     <int>
## 1 11111       119
## 2 113         119
## 3 114         105
## 4 115         120
#DISCARD participants from conditions invalid for this study
exclude_condition <- df_participants %>% 
          filter(!condition %in% conditions) %>% 
          mutate(reason="invalid-condition")

ex_participants <- rbind(ex_participants, exclude_condition)
rm(exclude_condition)     

df_participants <- df_participants %>% 
  filter( ! subject %in% ex_participants$subject)

No data need to be excluded on account of condition.

1.1.3 Sessions

The (string) session code is embedded in the URL querystring by the experimenter to differentiate testing sessions in SONA from demo and other environment setup tasks.

#MANUALLY INSPECT sessions
df_participants %>% group_by(session) %>% 
  dplyr::summarize(n=n())
## # A tibble: 1 × 2
##   session      n
##   <fct>    <int>
## 1 wi22sona   463

Data from sessions not corresponding to valid sessions should be discarded.

#DISCARD participants from invalid sessions 
exclude_session <- df_participants %>% 
          filter(!session %in% sessions) %>% 
          mutate(reason="invalid-session")

ex_participants <- rbind(ex_participants, exclude_session)
rm(exclude_session)     

df_participants <- df_participants %>% 
  filter( ! subject %in% ex_participants$subject)

No data need to be excluded on account of session.

1.1.4 Browser Interaction Violations

Browser interaction data is recorded by jspsych allowing us to determine if subjects violate our instructions not to leave the browser tab (or exit fullscreen mode) during test. These incidents are recorded in jspsych interaction data object, and the number of violations is counted and added to the participant data file.

Due to eccentricity of the browser events captured, 1-2 browser violations can be captured even if the subject did not leave the browser window (eg. in case of resizing window to meet minimum requirements.)

#MANUALLY INSPECT violations
df_participants %>% group_by(violations) %>% 
  dplyr::summarize(n=n())
## # A tibble: 15 × 2
##    violations     n
##         <dbl> <int>
##  1        1     298
##  2        1.5    21
##  3        2      81
##  4        2.5     8
##  5        3      23
##  6        3.5     3
##  7        4       8
##  8        4.5     2
##  9        5       9
## 10        5.5     1
## 11        6       3
## 12        6.5     2
## 13        7       1
## 14        8       2
## 15        9       1
#DISCARD participants exceeding the threshold of browser interaction violations 
exclude_violations <- df_participants %>% 
          filter(violations > violation_threshold) %>% 
          mutate(reason="exceeded-violations")

ex_participants <- rbind(ex_participants, exclude_violations)
rm(exclude_violations)     

df_participants <- df_participants %>% 
  filter( ! subject %in% ex_participants$subject)

Thirty two participants were excluded for exceeding the maximum allowed number of browser interaction violations.

1.1.5 Effort

To assist in mitigating increased noise in data collected asynchronously from the UCSD student subject pool, we added explicit ratings of how much effort the participant expended on the task. This question was implemented as a multiple-choice drop-down on an ‘Effort’ page prior to the ‘Demographics’ survey at the end of the study. Subjects were given four options : (1) I tried my best on each question, (2) I tried my best on most questions, (3) I started out trying hard, but gave up at some point, (4) I didn’t try very hard, or rushed through the questions.

#MANUALLY INSPECT effort
df_participants %>% group_by(effort) %>% 
  dplyr::summarize(n=n())
## # A tibble: 4 × 2
##   effort                                                      n
##   <chr>                                                   <int>
## 1 I didn't try very hard, or rushed through the questions     3
## 2 I started out trying hard, but gave up at some point       31
## 3 I tried my best on each question                          264
## 4 I tried my best on most questions                         133

Participants answering with options I didn’t try very hard, or rushed through the questions or I started out trying hard, but gave up at some point are excluded from analysis.

#DISCARD participants who indicated they did not expend adequate effort on the study
exclude_effort <- df_participants %>% 
          filter(effort %in% effort_exclusion) %>% 
          mutate(reason="selfrated-effort")

ex_participants <- rbind(ex_participants, exclude_effort)
rm(exclude_effort)     

df_participants <- df_participants %>% 
  filter( ! subject %in% ex_participants$subject)

Thirty-four participants are excluded for low (self-rated) effort.

1.1.6 Attention Check

The 6th question in the study is non-discriminatory (can easily get correct answer regardless of strategy) and serves as an attention check question.

#MANUALLY INSPECT attention
df_participants %>% group_by(attn_check) %>% 
  dplyr::summarize(n=n())
## # A tibble: 2 × 2
##   attn_check     n
##   <fct>      <int>
## 1 FALSE         37
## 2 TRUE         360

Participants who answered the attention check question incorrectly should be excluded.

#DISCARD participants who indicated they did not expend adequate effort on the study
exclude_attn <- df_participants %>% 
          filter(attn_check == FALSE) %>% 
          mutate(reason="failed-attnchk")

ex_participants <- rbind(ex_participants, exclude_attn)
rm(exclude_attn)     

df_participants <- df_participants %>% 
  filter( ! subject %in% ex_participants$subject)

Thirty seven participants are excluded for failing the attention check question.

1.1.7 Items

Next, we need to discard item_level data for excluded participants.

ex_items <- df_items %>% 
  filter (subject %in% ex_participants$subject) 

df_items <- df_items %>% 
  filter (!subject %in% ex_participants$subject )

1.2 Validation

After all exclusions, we are left with the following number of participants per condition:

#MANUALLY INSPECT conditions
df_participants %>% group_by(condition) %>% 
  dplyr::summarize(n=n())
## # A tibble: 4 × 2
##   condition     n
##   <fct>     <int>
## 1 11111        88
## 2 113          86
## 3 114          88
## 4 115          98

Finally, we need to validate we have a complete set of items for all valid participants.

count(df_items)[[1]] == count(df_participants)[[1]]* n_items 
## [1] TRUE

2 Participants Codebook

#see https://cran.r-project.org/web/packages/codebook/vignettes/codebook_tutorial.html

#ADD VARIABLE METADATA
dict <- rio::import("input/dictionary_sgc4a_participants.csv", "csv") #import data dictionary
var_label(df_participants) <- dict %>% select(VARIABLE, DESCRIPTION) %>% dict_to_list() #add variable labels

#ADD DATASET METATDATA
metadata(df_participants)$name <- "Experimental PARTICIPANTS for study SGC4A"
metadata(df_participants)$description <- "Data for study SGC4A summarized at PARTICIPANT  level"
metadata(df_participants)$creator <- "Amy Rae Fox"
metadata(df_participants)$contact <- "amyraefox@gmail.com"
#{r, eval = checkMode() == "pdf"} #ONLY FOR PDF KNIT
codebook::skim_codebook(df_participants)
Data summary
Name data
Number of rows 360
Number of columns 37
_______________________
Column type frequency:
character 8
factor 15
numeric 14
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
explanation 0 1 0 422 2 359 0
effort 0 1 32 33 0 2 0
other 0 1 0 345 234 115 0
country 0 1 2 25 0 41 0
language 0 1 6 9 0 8 0
schoolyear 0 1 5 7 0 6 0
disability 0 1 0 130 160 39 0
starttime 0 1 24 24 0 360 0

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
subject 0 1 FALSE 360 075: 1, 07F: 1, 083: 1, 0BT: 1
study 0 1 FALSE 1 SGC: 360
condition 0 1 FALSE 4 115: 98, 111: 88, 114: 88, 113: 86
session 0 1 FALSE 1 wi2: 360
exp_id 0 1 FALSE 2 221: 301, 221: 59
sona_id 0 1 FALSE 356 347: 3, 319: 2, 368: 2, 268: 1
pool 0 1 FALSE 1 son: 360
mode 0 1 FALSE 1 asy: 360
attn_check 0 1 FALSE 1 TRU: 360, FAL: 0
major 0 1 FALSE 7 Soc: 257, Bio: 40, Hum: 21, Mat: 17
gender 0 1 FALSE 3 Fem: 260, Mal: 93, Oth: 7
browser 0 1 FALSE 1 chr: 360
os 0 1 FALSE 5 Mac: 247, Win: 109, Lin: 2, Chr: 1
status 0 1 FALSE 1 suc: 360
term 0 1 FALSE 1 win: 360

Variable type: numeric

skim_variable n_missing complete_rate mean sd min median max hist
difficulty 0 1 3.12 0.97 1.00 3.00 5.00 ▁▃▇▆▁
confidence 0 1 3.18 0.99 1.00 3.00 5.00 ▁▅▇▇▂
enjoyment 0 1 3.23 1.13 1.00 3.00 5.00 ▂▅▇▆▃
age 0 1 20.53 2.29 18.00 20.00 37.00 ▇▂▁▁▁
width 0 1 1512.24 255.52 1134.00 1440.00 3840.00 ▇▁▁▁▁
height 0 1 802.53 127.14 680.00 769.00 2036.00 ▇▁▁▁▁
violations 0 1 1.35 0.58 1.00 1.00 3.00 ▇▁▂▁▁
absolute_score 0 1 2.02 3.62 0.00 0.00 12.00 ▇▁▁▁▁
discriminant_score 0 1 -5.65 7.47 -12.25 -9.17 12.00 ▇▂▁▁▂
tri_score 0 1 2.93 4.81 0.00 1.00 15.00 ▇▁▁▁▁
orth_score 0 1 9.71 5.01 0.00 12.00 15.00 ▃▁▂▅▇
other_score 0 1 2.96 3.85 0.00 2.00 36.00 ▇▁▁▁▁
blank_score 0 1 0.30 0.73 0.00 0.00 6.00 ▇▁▁▁▁
totaltime_m 0 1 13.00 14.83 2.82 11.30 253.84 ▇▁▁▁▁ 
codebook(df_participants, #ONLY FOR HTML KNIT
         metadata_table = TRUE,
         detailed_variables = FALSE,
         detailed_scales = FALSE,
         metadata_json = FALSE,
         survey_overview = FALSE,
         missingness_report = FALSE)

2.0.1 Metadata

2.0.1.1 Description

Dataset name: Experimental PARTICIPANTS for study SGC4A

Data for study SGC4A summarized at PARTICIPANT level

Metadata for search engines

  • Date published: 2022-04-08

  • Creator:

name value
1 Amy Rae Fox
x
x
subject
study
condition
session
exp_id
sona_id
pool
mode
attn_check
explanation
effort
difficulty
confidence
enjoyment
other
age
country
language
schoolyear
major
gender
disability
browser
width
height
os
starttime
status
term
violations
absolute_score
discriminant_score
tri_score
orth_score
other_score
blank_score
totaltime_m

2.1 Codebook table

3 Items Codebook

#see https://cran.r-project.org/web/packages/codebook/vignettes/codebook_tutorial.html

#ADD VARIABLE METADATA
dict <- rio::import("input/dictionary_sgc4a_items.csv", "csv") #import data dictionary

var_label(df_items) <- dict %>% select(VARIABLE, DESCRIPTION) %>% dict_to_list() #add variable labels

#ADD DATASET METATDATA
metadata(df_items)$name <- "Experimental ITEMS for study SGC4A"
metadata(df_items)$description <- "Data for study SGC4A summarized at participant-item level"
metadata(df_items)$creator <- "Amy Rae Fox"
metadata(df_items)$contact <- "amyraefox@gmail.com"
#{r, eval = checkMode() == "pdf"} #ONLY FOR PDF EXPORT
skim_codebook(df_items) 
## Warning in sorted_count(x): Variable contains value(s) of "" that have been
## converted to "empty".
Data summary
Name data
Number of rows 5400
Number of columns 27
_______________________
Column type frequency:
character 4
factor 14
numeric 9
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
study 0 1 5 5 0 1 0
graph 0 1 10 10 0 1 0
question 0 1 26 87 0 15 0
answer 0 1 0 27 138 176 0

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
subject 0 1 FALSE 360 075: 15, 07F: 15, 083: 15, 0BT: 15
term 0 1 FALSE 1 win: 5400
pool 0 1 FALSE 1 son: 5400
mode 0 1 FALSE 1 asy: 5400
block 0 1 FALSE 2 ite: 4320, ite: 1080
explicit 0 1 FALSE 1 1: 5400
impasse 0 1 FALSE 1 1: 5400
grid 0 1 FALSE 4 5: 1470, 1: 1320, 4: 1320, 3: 1290
mark 0 1 FALSE 2 emp: 4080, 1: 1320
ixn 0 1 FALSE 1 1: 5400
relation 0 1 FALSE 10 end: 720, mee: 720, mid: 720, sta: 720
q 0 1 FALSE 15 1: 360, 2: 360, 3: 360, 4: 360
correct 0 1 FALSE 2 FAL: 3973, TRU: 1427
condition 0 1 FALSE 4 115: 1470, 111: 1320, 114: 1320, 113: 1290

Variable type: numeric

skim_variable n_missing complete_rate mean sd min median max hist
gwidth 0 1 600.00 0.00 600.00 600.00 600.00 ▁▁▇▁▁
gheight 0 1 600.00 0.00 600.00 600.00 600.00 ▁▁▇▁▁
time_elapsed_m 0 1 6.92 13.57 0.36 5.27 252.14 ▇▁▁▁▁
discriminant 0 1 -0.39 0.72 -1.58 -0.58 1.00 ▁▇▃▂▂
tri_score 0 1 0.45 0.69 0.00 0.00 2.00 ▇▁▂▁▂
orth_score 0 1 0.93 0.72 0.00 1.00 2.00 ▅▁▇▁▃
other_score 0 1 0.23 0.72 0.00 0.00 12.00 ▇▁▁▁▁
blank_score 0 1 0.03 0.16 0.00 0.00 1.00 ▇▁▁▁▁
rt_s 0 1 33.13 35.95 0.78 21.80 536.39 ▇▁▁▁▁ 
codebook(df_items,#ONLY FOR HTML EXPORT
         metadata_table = TRUE,
         detailed_variables = FALSE,
         detailed_scales = FALSE,
         metadata_json = FALSE,
         survey_overview = FALSE,
         missingness_report = FALSE)
## Warning in sorted_count(x): Variable contains value(s) of "" that have been
## converted to "empty".

3.0.1 Metadata

3.0.1.1 Description

Dataset name: Experimental ITEMS for study SGC4A

Data for study SGC4A summarized at participant-item level

Metadata for search engines

  • Date published: 2022-04-08

  • Creator:

name value
1 Amy Rae Fox
x
x
subject
study
term
pool
mode
block
explicit
impasse
grid
mark
ixn
gwidth
gheight
graph
time_elapsed_m
question
relation
q
correct
discriminant
tri_score
orth_score
other_score
blank_score
answer
rt_s
condition

3.1 Codebook table

4 Data Export

4.1 Save Exclusions

For transparency, we save and identify the excluded data.

write.csv(ex_participants,"output/excluded_participants_winter22_sgc4a.csv", row.names = FALSE)
write.csv(ex_items,"output/excluded_items_winter22_sgc4a.csv", row.names = FALSE)

4.2 Analysis-Ready Files

#save participant file

write.csv(df_participants,"output/winter22_sgc4a_participants.csv", row.names = FALSE)

#save item file
write.csv(df_items,"output/winter22_sgc4a_items.csv", row.names = FALSE)